11,093 research outputs found
Parallelizing RRT on large-scale distributed-memory architectures
This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance
Parallel Toolkit for Measuring the Quality of Network Community Structure
Many networks display community structure which identifies groups of nodes
within which connections are denser than between them. Detecting and
characterizing such community structure, which is known as community detection,
is one of the fundamental issues in the study of network systems. It has
received a considerable attention in the last years. Numerous techniques have
been developed for both efficient and effective community detection. Among
them, the most efficient algorithm is the label propagation algorithm whose
computational complexity is O(|E|). Although it is linear in the number of
edges, the running time is still too long for very large networks, creating the
need for parallel community detection. Also, computing community quality
metrics for community structure is computationally expensive both with and
without ground truth. However, to date we are not aware of any effort to
introduce parallelism for this problem. In this paper, we provide a parallel
toolkit to calculate the values of such metrics. We evaluate the parallel
algorithms on both distributed memory machine and shared memory machine. The
experimental results show that they yield a significant performance gain over
sequential execution in terms of total running time, speedup, and efficiency.Comment: 8 pages; in Network Intelligence Conference (ENIC), 2014 Europea
Parallel Searching for a First Solution
A parallel algorithm for conducting a search for a first solution to the problem of generating minimal perfect hash functions is presented. A message-based distributed memory computer is assumed as a model for parallel computations. A data structure, called reverse trie (r-trie), was devised to carry out the search. The algorithm was implemented on a transputer network. The experiments showed that the algorithm exhibits a consistent and almost linear speed-up. The r-trie structure proved to be highly memory efficient
Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)
We explore the use of the Cell Broadband Engine (Cell/BE for short) for
combinatorial optimization applications: we present a parallel version of a
constraint-based local search algorithm that has been implemented on a
multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16
SPUs per blade). This algorithm was chosen because it fits very well the
Cell/BE architecture and requires neither shared memory nor communication
between processors, while retaining a compact memory footprint. We study the
performance on several large optimization benchmarks and show that this
achieves mostly linear time speedups, even sometimes super-linear. This is
possible because the parallel implementation might explore simultaneously
different parts of the search space and therefore converge faster towards the
best sub-space and thus towards a solution. Besides getting speedups, the
resulting times exhibit a much smaller variance, which benefits applications
where a timely reply is critical
A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks
Exact Bayesian structure discovery in Bayesian networks requires exponential
time and space. Using dynamic programming (DP), the fastest known sequential
algorithm computes the exact posterior probabilities of structural features in
time and space, if the number of nodes (variables) in the
Bayesian network is and the in-degree (the number of parents) per node is
bounded by a constant . Here we present a parallel algorithm capable of
computing the exact posterior probabilities for all edges with optimal
parallel space efficiency and nearly optimal parallel time efficiency. That is,
if processors are used, the run-time reduces to
and the space usage becomes per
processor. Our algorithm is based the observation that the subproblems in the
sequential DP algorithm constitute a - hypercube. We take a delicate way
to coordinate the computation of correlated DP procedures such that large
amount of data exchange is suppressed. Further, we develop parallel techniques
for two variants of the well-known \emph{zeta transform}, which have
applications outside the context of Bayesian networks. We demonstrate the
capability of our algorithm on datasets with up to 33 variables and its
scalability on up to 2048 processors. We apply our algorithm to a biological
data set for discovering the yeast pheromone response pathways.Comment: 32 pages, 12 figure
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Fine-grain loop scheduling for MIMD machines
Previous algorithms for parallelizing loops on MIMD machines have been based on assigning one or more loop iterations to each processor, introducing synchronization as required. These methods exploit only iteration level parallelism, and ignore the parallelism that may exist at a lower level.In order to exploit parallelism both within and across iterations, our algorithm analyzes and schedules the loop at the statement level. The loop schedule reflects the expected communication and synchronization costs of the target machine. We provide test results that show that this algorithm can produce good speedup of loops on an MIMD machine
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